Demonstration of a Very Inexpensive, Turbidimetric, Real-Time, RT ...
Experimental and computational approaches to estimate ......drug discovery and drug development...
Transcript of Experimental and computational approaches to estimate ......drug discovery and drug development...
advanced
drug delivery reviews
Advanced Drug Delivery Reviews 23 (1997) 3-25
Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings
Christopher A. Lipinski”, Franc0 I,ombardo, Beryl W. Dominy, Paul J. Feeney
(‘e,itrrr/ KYwu).ch I~ivitror~, t’fixv Iw., Gvorm. CT 06.340, USA
Kecewed 9 August 1996: accepted 14 August 1996
Abstract
Experimental and computational approaches tn estimate soluhility and permeability in discovery and development settings are described. In the discovery setting ‘the rule of 5’ predicts that poor absorption or permeation is more likely when there are more than 5 H-bond donors. 10 H-bond acceptors, the molecular weight (MWT) is greater than 500 and the calculated Log P (CLogP) is greater than 5 (or MlogP > 4.15). Computational methodology for the rule-based Moriguchi Log P (MLogP) calculation is described. Turbidimetric solubility measurement is described and applied to known drugs. High throughput screening (HTS) leads tend to have higher MWT and Log P and lower turbidimetric solubility than leads in the pre-HTS era. In the development setting, solubility calculations focus on exact value prediction and are difficult because of polymorphism. Recent work on linear free energy relationships and Log P approaches are critically reviewed. Useful predictions are possible in closely related analog series when coupled with experimental thermodynamic solubility measurements.
Kevworcfst Rule of 5; Computational alert; Poor absorption or permeation; MWT; MLogP; H-Bond donors and acceptors; Turbidimetric solubility; Thermodynamic solubility: Solubility calculation
I. lntroducnon .............................................................................................................................................. .......... 2. The drug diwwery setting ........................................................... .............................................. ......
2. I. Changes m drug leads and physic+chemrcal pmpernes ................................................... ........... ...... 7.2. Factor% affectmg physlco-chemxal lead profiles ........... ................................................................................... ........... 2. 3. Identlfylng a library with favorable physlco-chemical propertIe\. ......................................................................................
2.4. The target audience - medlcinal chemists.. ................................................................................................................... 2.5. Calculated propertleh of the ‘USAN’ llhrary ................................................................................................................... 2.h. The ‘rule of 5’ and Its implementation ...... .................. .................................................................................................
2.7. Orally actwe drugs outside the ‘rule of 5’ mnemomc and hlologlc transporters ...................................................................
2.X. Hrgh MWT USANs and the trend rn MLogP ................ ..................................................................................................... 2.9. New chemical entities. calculations ,,,..,...,,....,..,,..,,........,,..,,,.............,....................,,,..,,............,,, .................................... 2. IO. Drugs in absorption and permeability studies. calculations ....... .,,,,................,, ..................................................................
2. I I, Validating the computational alert ..,.,,..,,,................,.,.............,,,.,,.............,,,.., ................................................................ 2.12. Changes in calculated physical property profiles at Pfizer ,,..,,...............,,.,.................,......................,, ............................... 2.13. The rationale for measuring drug solubility in a discovery setting ,,, ................ .................................................................. 2.14. Drugs have high turbidimetric solubility ......... . ................... . . . . .................................. . . .. . . ......... . .... . . . . ...............................
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‘Correspondmg author. Tel: + I 860 44 I356 I: e-mall: [email protected].
0169-409X/97/$32.00 Copyright 0 1997 Elsevier Science B.V. All rightc reserved
P/I SOlh9-409X(96)00423-1
2. IS. High throughput xreening hits. clrlculations and aoluhilq nw~suren~rnt~ ... .......... ...........................................................
2.16. The triad of potency, volubility and permeability ............................ .............................................................................
7.17. Protocols for meaaurmg drug volubility in n discovery \ettmg ......................................................................................... 2. IX. Technical considerations and signal processing ...................................... .......................................... ..............................
3. Calculation of absorption parameter\ .............................................. ......................................................... ............................ 3. I. Overall approach.. ..................................................... ............ ................ .................................................................... 3.2. MLogP. Log P by the method of Moriguchi ........................................................................ ............................................. 3.3. MLogP calculation, ............................................... ........... ............. ...............................................................................
4. The development settmg: prediction of aqueous thermodynamic volubility .................................................................................. 4. I. General considerations ............................................................................ ........................................................................
4.2. LSERs and TLSER methods .............................................................................................................................................
4.3. LogP and AQUAFAC method\ ....... ................................................................................................................................
3.4. Other calculation methods ............................................................................................................................................... 5. Conclusion ........................................................................................................................................................................
Reference5 .................................................................................................................................................................................
1. Introduction 2. The drug discovery setting
This review presents distinctly different but com- plementary experimental and computational ap-
proaches to estimate solubility and permeability in drug discovery and drug development settings. In the discovery setting, we describe an experimental ap-
proach to turbidimetric solubility measurement as well as computational approaches to absorption and permeability. The absence of discovery experimental
approaches to permeation measurements reflects the authors’ experience at Pfizer Central Research. Ac- cordingly, the balance of poor solubility and poor
permeation as a cause of absorption problems may be signiticantly different at other drug discovery locations, especially if chemistry focuses on peptidic- like compounds. This review deals only with solu- bility and permeability as barriers to absorption. lntestinal wall active transporters and intestinal wall
metabolic events that influence the measurement of drug bioavailability are beyond the scope of this review. We hope to spark lively debate with our hypothesis that changes in recent years in medicinal chemistry physical property profiles may be the result of leads generated through high throughput screening. In the development setting, computational approaches to estimate solubility are critically re- viewed based on current computational solubility research and experimental solubility measurements.
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2.1. Changes in drug leads and physico-chemical
properties
In recent years, the sources of drug leads in the pharmaceutical industry have changed significantly.
From about 1970 on, what were considered at that time to be large empirically-based screening pro- grams became less and less important in the drug
industry as the knowledge base grew for rational drug design [ I]. Leads in this era were discovered using both in vitro and primary in vivo screening
assays and came from sources other than massive primary in vitro screens. Lead sources were varied coming from natural products; clinical observations
of drug side effects [ I]; published unexamined patents; presentations and posters at scientific meet- ings: published reports in scientific journals and collaborations with academic investigators. Most of
these lead sources had the common theme that the ‘chemical lead’ already had undergone considerable scientific investigation prior to being identified as a
drug lead. From a physical property viewpoint, the most poorly behaved compounds in an analogue series were eliminated and most often the starting lead was in a range of physical properties consistent with the previous historical record of discovering orally active compounds.
C.A. Lipirwki et cd. I Advanced Drug Drliver~ Rr~krs Z? (1997) .i-.?.5 5
This situation changed dramatically about 1989-
1991. Prior to 1989, it was technically unfeasible to
screen for in vitro activity across hundreds of
thousands of compounds, the volume of random screening required to efficiently discover new leads. With the advent of high throughput screening in the
1989-1991 time period, it became technically feas-
ible to screen hundreds of thousands of compounds across in vitro assays [2-41. Combinatorial chemis-
try soon began’ and allowed automated synthesis of
massive numbers of compounds for screening in the new HTS screens. The process was accelerated by
the rapid progress in molecular genetics which made
possible the expression of animal and human re- ceptor subtypes in cells lacking receptors that might
interfere with an assay and by the construction of receptor constructs to facilitate signal detection. The
screening of very large numbers of compounds necessitated a radical departure from the traditional
method of drug solubilization. Compounds were no longer solubilized in aqueous media under thermo-
dynamic equilibrating conditions. Rather, compounds
were dissolved in dimethyl sulfoxide (DMSO) as stock solutions, typically at about 20-30 mmol and
then were serially diluted into 96-well plates for assays (perhaps with some non ionic surfactant to improve solubility). In this paradigm, even very
insoluble drugs could be tested because the kinetics
of compound crystallization determined the apparent ‘solubility’ level. Moreover, compounds could parti-
tion into assay components such as membrane particulate material or cells or could bind to protein
attached to the walls of the wells in the assay plate. The net effect was a screening technology for
compounds in the pM concentration range that was largely divorced from the compounds true aqueous thermodynamic solubility. The apparent ‘solubility’
in the HTS screen is always higher, sometimes dramatically so, than the true thermodynamic solu- bility achieved by equilibration of a well character- ized solid with aqueous media. The in vitro HTS testing process is quite reproducible and potential problems related to poor compound solubility are
‘A search through SciSearch and Chemical Abstracts for refer-
ences to combinatorial chemistry in titles or descriptors using the
truncated terms COMBIN’? and CHEMISTR? gave the following
number of references respectively: 1990, 0 and 0; 199 I, 2 and I ; 1993. 8 and 8; 1994, I2 and II: 1995. 46 and 4.5.
often compensated for by the follow-up to the
primary screen. This is typically a more careful,
more labor-intensive process of in vitro retesting to determine ICSOs from dose response curves with
more attention paid to solubilization. The net result
of all these testing changes is that in vitro activity is reliably detected in compounds with very poor
thermodynamic solubility properties. A corollary result is that the measurement of the true thermo-
dynamic aqueous solubility is not very relevant to
the screening manner in which leads are detected.
2.2. Factors affecting physico-chenzical lead
profiles
The physico-chemical profile of current leads i.e.
the ‘hits’ in HTS screens now no longer depends on compound solubility sufficient for in vivo activity
but depends on: (1) the medicinal chemistry princi- ples relating structure to in vitro activity; (2) the
nature of the HTS screen; (3) the physico-chemical
profile of the compound set being screened and (4)
to human decision making, both overt and hidden as to the acceptability of compounds as starting points
for medicinal chemistry structure activity relation- ship (SAR) studies.
One of the most reliable methods in medicinal chemistry to improve in vitro activity is to incorpo-
rate properly positioned lipophilic groups. For exam- ple, addition of a single methyl group that can
occupy a receptor ‘pocket’ improves binding by about 0.7 kcal/mol [6]. By way of contrast, it is
generally difficult to improve in vitro potency by
manipulation of the polar groups that are involved in ionic receptor interactions. The interaction of a polar group in a drug with solvent versus interaction with
the target receptor is a ‘wash’ unless positioning of the polar group in the drug is precise. The traditional lore is that the lead has the polar groups in the correct (or almost correct) position and that in vitro potency is improved by correctly positioned lipo- philic groups that occupy receptor pockets. Polar
groups in the drug that are not required for binding can be tolerated if they occupy solvent space but they do not add to receptor binding. The net effect of these simple medicinal chemistry principles is that, other factors being equal, compounds with correctly positioned polar functionality will be more readily
detectable in HTS screens if they are larger and more
lipophilic. The nature of the screen determines the physico-
chemical profile of the resultant ‘hits’. The larger the
number of hits that are detected, the more the
physico-chemical profile of the ‘hits’ resembles the
overall compound set being screened. Technical
factors such as the design of the screen and human cultural factors such as the stringency of the evalua-
tion as to what is a suitable lead worth are majol determinants of the physico-chemical profiles of the
eventual leads. Screens designed with very high specificity. for example many receptor based assays.
generate small numbers of hits in the PM range. In these types of screens the signal is easy to detect
against background noise. the hits are few or can be
made few by altering potency criteria and the
physico-chemical profiles tend towards more lipo-
philic, larger, less soluble compounds. Tight control
of the criteria for activity detection in the initial HTS screen minimizes labor-intensive secondary evalua- tion and minimizes the effect of human biases. The
downside is that lower potency hits with more favorable physico-chemical property protiles may be
discarded. Cell-based assays, by their very nature tend to
produce more ‘hits’ than receptor-based screens. These types of assays monitor a functional event, fol
example a change in the level of a signaling inter- mediate or the expression level of M-RNA or
protein. Multiple mechanisms may lead to the mea-
sured end point and only a few of these mechanisms may be desirable. This leads to a larger number of hits and therefore their physico-chemical profile will
more closely resemble that of the compound set being screened. Perhaps, equally importantly. a larger volume of secondary evaluation allows for a greater expression of human bias. Bias is especially difficult to quantify in the chemists perception of a
desirable lead structure. The physico-chemical profile of the compound set
being screened is the first tilter in the physico- chemical profile of an HTS ‘hit’. Obviously high
molecular weight, high lipophilicity compounds will not be detected by a screen if they are not present in the library. In the real world, trade-offs occur in the choice of profiles for compound sets. An exclusively low molecular weight, low lipophilicity library likely increases the difficulty of detecting ‘hits’ but sim-
plifies the process of discovering an orally active drug once the lead is identified. The converse is true of a high molecular weight high lipophilicity library.
In our experience, commercially available (non combinatorial) compounds like those available from
chemical supply houses tend towards lower molecu- lar weights and lipophilicities.
Human decision making, both overt and hidden
can play a large part in the profile of HTS ‘hits’. For example. a requirement that ‘hits’ possess an accept-
able range of measured or calculated physico-chemi- cal properties will obviously affect the starting
compound profiles for medicinal chemistry SAR. Lesh obvious are hidden biases. Are the criteria for a ‘hit’ changing to higher potency (lower IC.50) as the
HTS screen runs? Labor-intensive secondary follow-
up is decreased but less potent, perhaps physico- chemically more attractive leads, may be eliminated.
How do chemists react to potential lead structures?
In an interesting experiment, we presented a panel of our most experienced medicinal chemists with a
group of theoretical lead structures - all containing literature ‘toxic’ moieties. Our chemists split into two very divergent groups; those who saw the toxic
moieties as a bar to lead pursuit and those who recognized the toxic moiety but thought they might be able to replace the offending moiety. An easy way to illustrate the complexity of the chemists percep-
tion of lead attractiveness is to examine the re- markably diverse structures of the new chemical
entities (NCEs) introduced to market that appear at
the back of recent volumes of Annuul Reports in
Mrdicind C’hemistry. No single pharmaceutical com- pany can conduct research in all therapeutic areas
and so some of these compounds, which are all marketed drugs, will inevitably be less familiar and potentially less desirable to the medicinal chemist at one research location, but may be familiar and desirable to a chemist at another research site.
The idea in selecting a library with good absorp- tion properties is to use the clinical Phase II selection process as a filter. Drug development is expensive and the most poorly behaved compounds are weeded out early. Our hypothesis was that poorer physico- chemical properties would predominate in the many
C.A. Lipiwki et a/. I Advancrd Drug Drlivm Reviw~ 2.3 (1997) -3-2.5 1
compounds that enter into and fail to survive pre-
clinical stages and Phase I safety evaluation. We
expected that the most insoluble and poorly perme- able compounds would have been eliminated in those
compounds that survived to enter Phase II efficacy studies. We could use the presence of United States
Adopted Name (USAN) or International Non-pro- prietary Name (INN) names to identify compounds entering Phase II since most drug companies (includ-
ing Pfizer) apply for these names at entry to Phase II.
The (WDI) World Drug Index is a very large computerized database of about 50 000 drugs from the Derwent Co. The process used to select a subset of 2245 compounds from this database that are likely
to have superior physico-chemical properties is as follows: From the 50 427 compounds in the WDl File. 7894 with a data field for a USAN name were
selected as were 6320 with a data field for an INN. From the two lists, 8548 compounds had one or both USAN or INN names. These were searched for a
data held ‘indications and usage’ suggesting clinical
exposure, resulting in 3704 entries. From the 3704 using a substructure data field we eliminated 1176 compounds with the text string ‘POLY’, 87 with the text string ‘PEPTIDE’ and 101 with the text string ‘QUAT’. Also eliminated were 53 compounds con- taining the fragment 0 = P-O. We coined the term ‘USAN’ library for this collection of drugs.
2.4. The target audience - medicinal chemists
Having identified a library of drugs selected by the economics of entry to the Phase II process we sought to identify calculable parameters for that library that
were likely related to absorption or permeability. Our
approach and choice of parameters was dictated by very pragmatic considerations. We wanted to set up an absorption-permeability alert procedure to guide our medicinal chemists. Keeping in mind our target audience of organic chemists we wanted to focus on the chemists very strong pattern recognition and chemical structure recognition skills. If our target
audience had been pharmaceutical scientists we would not have deliberately excluded equations or regression coefficients. Experience had taught us that a focus on the chemists very strong skills in pattern
recognition and their outstanding chemistry structural recognition skills was likely to enhance information transfer. In effect, we deliberately emphasized en-
hanced educational effectiveness towards a well
defined target audience at the expense of a loss of
detail. Tailoring the message to the audience is a
basic communications principle. One has only to look at the popular chemistry abstracting booklets with their page after page of chemistry structures and
minimal text to appreciate the chemists structural
recognition skills. We believe that our chemists have accepted our calculations at least in part because the calculated parameters are very readily visualized
structurally and are presented in a pattern recognition
format.
2.5. Calculated properties of the ‘USAN’ library
Molecular weight (formula weight in the case of a
salt) is an obvious choice because of the literature
relating poorer intestinal and blood brain barrier permeability to increasing molecular weight [7,8]
and the more rapid decline in permeation time as a function of molecular weight in lipid bi-layers as
opposed to aqueous media 191. The molecular
weights of compounds in the 2245 USANs were lower than those in the whole 50 427 WDI data set. In the USAN set 11% had MWTs > 500 compared to 22% in the entire data set. Compounds with MWT > 600 were present at 8% in the USAN set compared to 14% in the entire data set. This difference is not
explainable by the elimination of the very high MWTs in the USAN selection process. Rather it
reflects the fact that higher MWT compounds are in general less likely to be orally active than lower MWTs.
Lipophilicity expressed as a ratio of octanol solubility to aqueous solubility appears in some form
in almost every analysis of physico-chemical prop-
erties related to absorption [ 101. The computational problem is that an operationally useful computational alert to possible absorption-permeability problems must have a no fail log P calculation. In our
experience, the widely used and accurate Pomona College Medicinal Chemistry program applied to our compound file failed to provide a calculated log P (CLogP) value because of missing fragments for at least 25% of compounds. The problem is not an inordinate number of ‘strange fragments’ in our chemistry libraries but rather lies in the direction of the trade off between accuracy and ability to calcu- late all compounds adopted by the Pomona College
team. The CLogP calculation emphasizes high ac- curacy over breadth of calculation coverage. The fragmental CLogP value is defined with reference to
five types of intervening isolating carbons between
the polar fragments. As common a polar fragment as a sulfide (6) linkage generates missing fragments
when flanked by rare combinations of the isolating
carbon types. Polar fragments as defined by the CLogP calculation can be very large and are not calculated as the sum of smaller, more common,
polar fragments. This approach enhances accuracy but increases the number of missing fragments.
We implemented the log P calculation (MLogP) air
described by Moriguchi et al. [ 1 I] within the Molec- ular Design Limited MACCS and ISIS base pro- grams to avoid the missing fragment problem. As ;I
rule-based system, the Moriguchi calculation always
gives an answer. The pros and cons of the Moriguchi algorithm have been debated in the literature [ 12,131.
We recommend that, within analog series, our medicinal chemists use the more accurate Pomona CLogP calculation if possible. For calculation or
tracking of library properties the less accurate MLogP program is used.
Only about 10% of USAN compounds have a
CLogP over 5. The CLogP value of 5 calculated on the USAN data set corresponds to an MLogP of
4.15. The slope of CLogP (X axis) versus MLogP (J axis) is less than unity. At the high log P end, the Moriguchi MLogP is somewhat lower than the
MedChem CLogP. In the middle log P range at about 2, the two scales are similar. Experimentally there is
almost certainly a lower (hydrophilic) log P limit to absorption and permeation. Operationally, we have ignored a lower limit because of the errors in the
MLogP calculation and because excessively hydro- philic compounds are not a problem in compounds originating in our medicinal chemistry laboratories.
An excessive number of hydrogen bond donot groups impairs permeability across a membrane bi- layer Il4,15]. Hydrogen donor ability can be mea- sured indirectly by the partition coefticient between strongly hydrogen bonding solvents like water OI
ethylene glycol and a non hydrogen bond accepting solvent like a hydrocarbon 1151 or as the log of the ratio of octanol to hydrocarbon partitioning. In vitro systems for studying intestinal drug absorption have been recently reviewed I16]. Computationally, hy- drogen donor ability differences can be expressed by the solvatochromic cy parameter of a donor group
with perhaps a steric modifier to allow for the interactions between donor and acceptor moieties. Experimental LY values for hydrogen bond donors and
6 values for acceptor groups 1171 have been com- piled by Professor Abraham in the UK and by the Raevsky group in Russia [ 18,191. Both research
groups currently express the hydrogen bond donor
and acceptor properties of a moiety on a thermo- dynamic free energy scale. In the Raevsky C scale, donors range from about - 4.0 for a very strong
donor to - 0.5 for a very weak donor. Acceptors
values in the Raevsky C scale are all positive and range from about 4.0 for a strong acceptor to about
0.5 for a weak acceptor. In the Abraham scale both donors and acceptors have positive values that are about one-quarter of the absolute C values in the
Raevsky scale. We found that simply adding the number of NH
bonds and OH bonds does remarkably well as an
index of H bond donor character. Importantly, this pa- rameter has direct structural relevance to the chemist. When one looks at the USAN library there is a sharp cutoff in the number of compounds containing more
than 5 OHS and NHs. Only 8% have more than 5. SO 92% of compounds have five or fewer H bond donors
and it is the smaller number of donors that the litera- ture links with better permeability.
Too tnany hydrogen bond acceptor groups also hinder permeability across a membrane bi-layer. The
sum of Ns and OS is a rough measure of H bond accepting ability. This very simple calculation is not
nearly as good as the OH and NH count (as a model for donor ability) because there is far more variation in hydrogen bond acceptor than donor ability across
atom types. For example, a pyrrole and pyridine nitrogen count equally as acceptors in the simple N 0 sum calculation even though a pyridine nitrogen is a very good acceptor (2.72 on the C scale) and the pyrrole nitrogen is an far poorer acceptor (1.33 on the C scale). The more accurate solvatochromic p
parameter which measures acceptor ability varies far more on a per nitrogen or oxygen atom basis than the corresponding LY parameter. When we examined the USAN library we found a fairly sharp cutoff in protiles with only about 12% of compounds having more than 10 Ns and OS.
-7.6. Thr ‘r-ulr of 5’ cml its implementation
At this point we had four parameters that we
C.A. Lipinski ef trl. I Advancrd Drug Drlivrry RrrGws 23 (1997) 3-2.5 9
thought should be globally associated with solubility and permeability; namely molecular weight; Log P;
the number of H-bond donors and the number of H-bond acceptors. In a manner similar to setting the
confidence level of an assay at 90 or 95% we asked
how these four parameters needed to be set so that
about 90% of the USAN compounds had parameters in a calculated range associated with better solubility
or permeability. This analysis led to a simple
mnemonic which we called the ‘rule of 5’ [20]
because the cutoffs for each of the four parameters were all close to 5 or a multiple of 5. In the USAN
set we found that the sum of Ns and OS in the molecular formula was greater than 10 in 12% of the
compounds. Eleven percent of compounds had a MWT of over 500. Ten percent of compounds had a
CLogP larger than 5 (or an MLogP larger than 4.15)
and in 8% of compounds the sum of OHS and NHs
in the chemical structure was larger than 5. The ‘rule of 5’ states that: poor absorption or permeation are more likely when:
There are more than 5 H-bond donors (expressed as the sum of OHS and NHs); The MWT is over 500;
The Log P is over 5 (or MLogP is over 4.15); There are more than 10 H-bond acceptors (ex- pressed as the sum of Ns and OS)
Compound classes that are substrates for bio- logical transporters are exceptions to the rule.
When we examined combinations of any two of the four parameters in the USAN data set, we found that combinations of two parameters outside the
desirable range did not exceed 10%. The exact values from the USAN set are: sum of N and 0 + sum of NH and OH - 10%; sum of N and 0 + MWT - 7%; sum of NH and OH + MWT -
4% and sum of MWT + Log P - 1%. The rarity (1%) among USAN drugs of the combination of high MWT and high log P was striking because this particular combination of physico-chemical proper-
ties in the USAN list is enhanced in the leads
resulting from high throughput screening. The rule of 5 is now implemented in our registra-
tion system for new compounds synthesized in our
medicinal chemistry laboratories and the calculation program runs automatically as the chemist registers a
new compound. If two parameters are out of range, a ‘poor absorption or permeability is possible’ alert
appears on the registration screen. All new com-
pounds are registered and so the alert is a very
visible educational tool for the chemist and serves as
a tracking tool for the research organization. No
chemist is prevented from registering a compound because of the alert calculation.
2.7. 0rull.v active drugs outside the ‘rule of 5
mnemonic and biologic tran.rporter.s
The ‘rule of 5’ is based on a distribution of calculated properties among several thousand drugs.
Therefore by definition, some drugs will lie outside
the parameter cutoffs in the rule. Interestingly, only a
small number of therapeutic categories account for most of the USAN drugs with properties falling outside our parameter cutoffs. These orally active therapeutic classes outside the ‘rule of 5’ are:
antibiotics, antifungals, vitamins and cardiac glyco-
sides. We suggest that these few therapeutic classes contain orally active drugs that violate the ‘rule of 5’
because members of these classes have structural features that allow the drugs to act as substrates for naturally occurring transporters. When the ‘rule of 5’
is modified to exclude these few drug categories only
a very few exceptions can be found. For example. among the NCEs between 1990 and 1993 falling
outside the double cutoffs in ‘the rule of 5’, there were nine non-orally active drugs and the only orally active compounds outside the double cutoffs were seven antibiotics. Fungicides-protoazocides-antisep-
tics also fall outside the rule. For example, among the 4 I USAN drugs with MWT > 500 and MLogP > 4.15 there were nine drugs in this class. Vitamins are
another orally active class drug with parameter values outside the double cutoffs. Close to 100 vitamins fell into this category. Cardiac glycosides, an orally active drug class also fall outside the parameter limits of the rule of 5. For example among 90 USANs with high MWT and low MLogP there were two cardiac glycosides.
2.8. High MWT USANs and thr trend in MLogP
In our USAN data set we plotted MLogP against
MWT and examined the compound distributions as
defined by the 50 and 90% probability ellipses. A
large number of USAN compounds had MLogP
more negative than - 0.5. Among the USAN com- pounds there was a trend for higher MWT to
correlate with lower MLogP. This type of trend is distinctly different from the positive correlation
between MLogP and MWT found in most SAR data
sets. Usually as MWT increases, compound lipo- philicity increases and MLogP becomes larger (more
positive). From among the 2641 USANs. we selected
the 405 with MLogP more negative than - 0.5 and from among these selected those with MWT in
excess of 500 and mapped the resulting 90 against therapeutic activity fields in the MACCS WDI
database. About one half (44 of 90) of these high
MWT. low MLogP USANs were orally inactive
consisting of 26 peptide agonists or antagonists, I I quaternary ammonium salts and seven miscellaneous
non-orally active agents. Among the USAN compounds in our list fewer
than 10% of compounds had either high MLogP or
high MWT. The combination of both these prop- erties in the same compound was even rarer. Among
2641 USANs there were only 41 drugs with MWT > 500 and MLogP > 4.15. about one-half (21) were
orally inactive. Among the remainder there were only six orally active compounds not in the fungicide and vitamin classes.
2.9. Ne\v chemical entities, culrzlutions
New chemical entities introduced between 1990 and 1993 were identified from a summary listing in
vol. 29 of Annual Reports in Medicinal Chrmisty.
All our computer programs for calculating physico-
chemical properties require that the compound be described in computer-readable format. We mapped compound names and used structural searches to identify 133 of the NCEs in the Derwent World Drug to give us the computer-readable formats to calculate the rule of 5. The means of calculated properties were well within the acceptable range. The average Moriguchi log P was 1.80, the sum of H-bond donors was 2.53, the molecular weight was 408 and the sum of Ns and OS was 6.95. The incidence of alerts fol possible poor absorption or permeation was 12%.
2. IO. Drugs in absorption and permeability
studies. calculations
Very biased data sets are encountered in the types
of drugs that are reported in the absorption or permeability literature. Calculated properties are
quite favorable when compared to the profiles of
compounds detected by high throughput screening. Compounds that are studied are usually orally active marketed drugs and therefore by definition have
properties within the acceptable range. What is generally not appreciated is that absorption and
permeability are mostly reported for the older drugs.
For example, our list of compounds with published literature on absorption or permeability, studied
internally for validation purposes, is highly biased against NCEs. Only one drug in our list of 73 was
introduced in the period 1990 to date. In part this
reflects drug availability. since drugs under patent
are not sold by third parties. Drugs studied in absorption or permeability models tend to be those with value for assay validation purposes, i.e. those with considerable pre-existing literature. In addition.
sonic of the newer studies are driven by a regulatory
agency interest in the permeability properties of generic drugs. In our listing of 7.7 drugs in absorp- tion or permeability studies there are 33 generic
drugs whose properties the FDA is currently profi- ing. Our list includes an additional 23 drugs with
CACO-2 cell permeation data. Most of these are l’rom the speakers’ handouts at a recent meeting on
permeation prediction [ 211; a few are from internal Ptixer CACO-2 studies. A final I2 drugs are those with zwitterionic or very hydrophilic properties fol which there are either literature citations or internal Pfizer data. The means of calculated properties for compounds in this list are well within the acceptable
range. The average Moriguchi log P was 1.60, the wm of’ H-bond donors was 2.49. the molecular
weight was 361 and the sum of Ns and OS was 6.27. The incidence of alerts for possible poor absorption or permeation was 12% (Table I ).
2. It. Vuliduting the c.nmpututionul alert
Validating a computational alert for poor absorp- tion or permeation in a discovery setting is quite different than validating a quantitative prediction calculation in a developmental setting. In effect, a discovery alert is a very coarse filter that identifies
Table I
C.A. Lipinski et a/. I Advanced Drug Delivery Reviews 2.7 (1997) -3-2.5 II
Partial list of drugs in absorption and permeability studies
Drug name MLogP OH +NH’ MWT N+O” Alert”
Aciclovir” h Alprdzolam” Aspirinh Atenolol” ” Azithromycinh AZT” Benzyl-penicillin” Caffeine” Candoxatril” Captopril” Carbamazepine“ Chloramphenicol h Cimetidine” h Clonidine” Cyclosporine” Desipramine” ” Dexamethasoneh Diazepamh Diclofenac” Diltiazem-HCI Doxorubicin” Enalapril-maleate” Erythromycin” Fdmotidine Felodipine” h Fluorourdcil” Flurbiprofen ’ Furosemide” Glycine” Hydrochk;rthiazide” Ibuprofen Imipramine” Itraconazole” Ketaconazole” Ketoprofen” Labetalol-HCI” Lisinopril” Manmtol h Methotrexate” Metoprolol-tartrate” ” Nadolol” Naloxone” Naproxen-sodium” h Nortriptylene-HCI” Omeprazole” Phenytoin” Piroxicanl” PrilZosinh Propranolo-HCI“ h Quinidine” Ranitidine-HCI” Scopolamine” Tenidaoh Terfenadine Testosterone” Trovafloxacmh Valproic-acid” Vinblaatineh
- 0.09 4.14 I .70 0.92 0.14
-4.38 I .82
225.2 I 308.77 180.16 266.34 749.00 267.25 334.40 194.19 515.65 217.29 236.28
8 0 4 0 4 0 5 0
I4 I 9 0
6 6 : 8 4 : 3 0
: 0 0 3 0
23 I 2 s :: 3 0 3 0 6 0
0.20 3.03 0.64 3.53 I .23 323.14
252.34 230.10
1202.64
0.82 3.41
~0.32 3.64 I .8S 3.36 3.99 2.67
- I.33 I .64
-0.14 -0.18
3.22 - 0.63
3.90 0.95
- 3.44 ~ I .0x
3.23 3.88 5.53 4.45 3.37 2.67 I.1 I
-2.50
266.39 392.47 284.75 296. IS 414.s3 543.53 376.46 733.95 337.45 384.26
I2 7 1
5 8
I4 I 9 0 5 0 4 0 2 0 7 0 3 0 I 2 : 2 0
I2 I I 0 3 0 5 0 x 0 6 0
13 I 4 0 5 0 5 0 3 0 I 0 9 0
2 130.08 244.27 330.7s 4
75.07 291.74 206.29 280.42 705.6.5 380.92 254.29 328.42 40.5.50 182.18 454.45 267.37 309.4 I
4
0 0 0
6 I ?
I .60 1.6.5 0.97 f I.53 327.3X
230.27 2.76 4.14
-4.38 2.20
263.39 267.25 45 I .49 331.35 383.41 259.35
2 2
I 0 7 : 0.00
2.05 2.S3 2.19 0.66 I .42 I .9s 3 94
9 3 ?I 4 0 7 0 s 0 5 0 3 0 2 0 7 0
324.43 314.41 303.36 320.76 47 I .69 288.43 4 16.36 144.22 Xl 1.00
2 2
ii0 2.81 2.06 2.96
2 0 13 I
Ziprdsidone” 3.71
“Standard or drug in FDA bioequivalence study.
“Studied in CACO-2 permeation,
‘Sum of OH and NH H-bond donors.
“Sum of N and 0 H-bond acceptors.
I 4 12.95 5 0
‘Computational alert according to the rule of 5; 0. no problem detected; I, poor absorption or permeation are more likely.
compounds lying in a region of property space where
the probability of useful oral activity is very low.
The goal is to move chemistry SAR towards the
region of property space where oral activity is
reasonably possible (but not assured) and where the more labor-intensive techniques of drug metabolism and the pharmaceutical sciences can be more effi-
ciently employed. A compound that fails the compu- tational alert will likely be poorly bio-available
because of poor absorption or permeation and lies
within that region of property space where good absorption or solubility is unlikely. We believe the alert has its primary value in identifying problem
compounds. In our experience. most compounds
failing the alert also will prove troublesome if they
progress far enough to be studied experimentally. However, the converse is not true. Compounds
passing the alert still can prove troublesome in experimental studies.
In this perspective. a useful computational alert correctly identities drug projects with known absorp-
tion problems. Drugs in human therapy. whethet poorly or well absorbed from the viewpoint of the pharmaceutical scientist, should profile as ‘drugs’. i.e. as having reasonable prospects for oral activity. The larger the computational and experimental dil-
ference between drugs in human therapy and those
which are currently being made in medicinal chemis- try laboratories. the greater the confidence that the differences are meaningful. We assert that absorption problems have recently become worse in the pharma- ceutical industry ax attested to by recent meetings
and symposia on this subject 1221 and by the informal but industry-wide concern of pharmaceu-
tical scientists about drug candidates with less than optimal physical properties. If we are correct. within
any drug organization. one should be able to quantify by calculation whether time-dependent changes that might impair absorption have occurred in medicinal
chemistry. If these changes have occurred one can try to correlate the\e with changes in screening strategy.
How relevant is our experience at the Pfizer Central Research laboratories in Groton to what may be expected to be observed in other drug discovery
organizations? The physical property profiles of drug leads discovered through HTS will be similar indus-
try-wide to the extent that testing methodology,
selection criteria and the compounds being screened are similar. Changes in physical property profiles of
synthetic compounds, made in follow-up of HTS leads by medicinal laboratories, depend on the timing of a major change towards HTS screening.
The Pfizer laboratories in Groton were one of the first to realize and implement the benefits of HTS in lead detection. As a consequence, we also have been
one of the first to deal with the effects of this change in screening strategy on physico-chemical properties.
In Groron, 1989 marked the beginning of a signih- cant change towards HTS screening. This process
was largely completed by 1992 and currently HTS is now the major, rich source of drug discovery leads
and has largely supplanted the pre-1989 pattern of lead generation.
At the Pfizer Groton site, we have retrospectively examined the MWT distributions of compounds
made in the pre- 1989 era and since 1989. Since our registration systems unambiguously identify the source of each compound, we can identify any time- dependent change in physical properties and we can
compare the profiles of internally synthesized com- pounds with the profiles of compounds purchased
from external commercial sources. Before 1989. the percentage of internally syn-
thesized high MWT compounds oscillated in a range
very similar IO the USAN library (Table 2). Starting in 1989. there was an upward jump in the percentage of high MWT compounds and a further jump in 1992
IO a new stable MWT plateau that is higher than in
Yex Iregistered Synthetx compounds Commercial compounds
C.A. Lipinski et ul. I Advowed Drug Delivery Revirws 23 (1997) 3-2.5 13
the USAN library and higher than any yearly oscilla-
tion in the pre-1989 era. By contrast, there was no
change in the MWT profiles of commercially pur- chased compounds over the same time period. A
comparison of the MWT and MLogP percentiles of synthetic compounds for a year before the advent of
HTS and for 1994 in the post-HTS era shows a similar pattern (Table 3). The upper range percen- tiles for MWT and MLogP properties are skewed towards physical properties less favorable for oral
absorption in the more recent time period. The trend towards higher MWT and LogP is in the
direction of the property mix that is least populated in the USAN library. There was no change over time
in the population of compounds with high numbers of H-bond donors or acceptors.
2. J-3. Thr rationale ,for measuring drug solubilit)
in a discover?/ setting
In recent years, we have been exploring ex-
perimental protocols in a discovery setting that measure drug solubility in a manner as close as
possible to the actual solubilization process used in our biological laboratories. The rationale is that the
physical forms of the compounds solubilized and the methods used to solubilize compounds in discovery
are very different from those used by our pharma- ceutical scientists and that mimicking the discovery
process will lead to the best prediction of in vivo SAR.
In discovery, the focus is on keeping a drug solubilized for an assay rather than on determining the solubility limit. Moreover, there is no known
automated methodology that can efficiently solubil- ize hundreds of thousands of sometimes very poorly
soluble compounds under thermodynamic conditions.
In our biological laboratories, compounds that are not obviously soluble in water or by pH adjustment
Table 3
Synthetic compound properties in 1986 (pre-HTS) and 1994
(post-HTS)
Percentile MLogP MWT
I986 1994 1986 I994
90th 4.30 4.76 514 726
75th 3.48 3.90 41s 535
50th 2.60 2.86 352 412
are pre-dissolved in a water miscible solvent (most
often DMSO) and then added to a well stirred aqueous medium. The equivalent of a thermody-
namic solubilization, i.e. equilibrating a solid com- pound for 24-48 h, separating the phases, measuring
the soluble aqueous concentration and then using the
aqueous in an assay, is not done. When compounds
are diluted into aqueous media from a DMSO stock solution, the apparent solubility is largely kinetically
driven. The influence of crystal lattice energy and the effect of polymorphic forms on solubility is, of
course, completely lost in the DMSO dissolution
process. Drug added in DMSO solution to an aque- ous medium is delivered in a very high energy state
which enhances the apparent solubility. The appear-
ance of precipitate (if any) from a thermodynamical-
ly supersaturated solution is kinetically determined and to our knowledge is not predictable by computa-
tional methods. Solubility may also be perturbed
from the true thermodynamic value in purely aque- ous media by the presence of a low level of residual DMSO.
The physical form of the first experimental lot of a
compound made in a medicinal chemistry lab can be
very different from that seen by the pharmaceutical scientist at a later stage of development. Solution
spectra, HPLC purity criteria and mass spectral
analysis are quite adequate to support a structural assignment when the chemist’s priority is on effi-
ciently making as many well selected compounds as possible in sufficient quantity for in vitro and in vivo
screening. All the measurements that support struc- tural assignment are unaffected by the energy state (polymorphic form) of the solid. Indeed, depending on the therapeutic area, samples may not be crys-
talline and most compounds synthesized for the first
time are unlikely to be in lower energy crystalline forms. Attempts to compute solubility using melting point information are not useful if samples do not
have well defined melting points. Well characterized, low energy physical form (from a pharmaceutics viewpoint) reduces aqueous solubility and may
actually be counter productive to the discovery chemists priority of detecting in vivo SAR.
in this setting, thermodynamic solubility data can be overly pessimistic and may mislead the chemist who is trying to relate chemical structural changes to absorption and oral activity in the primary in vivo
assay. Our goal is to provide a relevant experimental
solubility measurement so that chemistry can move
from the pool of poorly soluble, orally inactive
compounds towards those with some degree of oral
activity. For maximum relevance to the in vivo biological assay our solubility measurement protocol
is as close as possible to the biological assay ‘solubilization’. In this paradigm, any problems that
might be related to the poor absorption of a low
energy crystalline solid under thermodynamic con-
ditions are postponed and not solved. The efficiency
gain in an early discovery stage solubility assay lies
in the SAR direction provided to chemistry and in
the more efficient application of drug metabolism and pharmaceutical sciences resources once oral
activity is detected. The value of this type of assay is
very stage-dependent and the discovery type of assay is not a replacement for a thermodynamic solubility
measurement at a later stage in the discovery pro- cess.
2.14. Drugs ha\!e high turhiditnetric soluhilit~
Measuring solubility by turbidimetry violates aI_
most every precept taught in the pharmaceutical
sciences about ‘proper’ thermodynamic solubility
measurement. Accordingly, we have been profiling known marketed drugs since our initial presentation on turbidimetric solubility measurement [23] and have measured turbidimetric solubilities on over 3.50
drugs from among those listed in the Derwent World Drug Index. The calculated properties of these drugs
are well within the favorable range for oral absorp- tion. The average of the calculated properties are:
MLogP, 1.79; the sum of OH and NH, 2.01: MWT. 295.4; the sum of N and 0. 4.69. Without regard to the therapeutic class, only 4% of these drugs would have been flagged as having an increased probability
of poor absorption or permeability in our computa- tional alert. Of the 353 drugs, 305 (87%) had a turbidimetric solubility of greater than 65 kg/ml. There were only 20 drugs (7%) with a turbidimetric solubility of 20 pg/ml or less. If turbidimetric solubility values lie in this low range, we suggest to our chemists that the probability of useful oral activity is very low unless the compound is unusual- ly potent (e.g. projected clinical dose of 0.1 mg/kg) or unusually permeable (top tenth percentile in absorption rate constant) or unless the compound is a
member of a drug class that is a substrate for a biological transporter.
Our drug list was compiled without regard to
literature thermodynamic solubilities but does con- tain many of the types of compounds studied in the absorption literature. Of the 353 drugs studied in the
discovery solubility assay, 171 are drugs from four
sources. There are 77 drugs from the compilation of
200 drugs by Andrews et al. [6]. This compilation is biased towards drugs with reliable measured in vitro
receptor affinity and with interesting functionality
and not necessarily towards drugs with good absorp tion or permeation characteristics. There are 23 drugs
from a list of generics whose properties FDA is
currently profiling for bio-equivalency standards. In addition, there are 42 NCEs introduced between 19X.1 and 1993 and 37 entries are for drugs with
CACO-2 cell permeation data. The protile of drug turbidimetric solubilities serves
as a useful benchmark. Compounds that are drugs
have a very low computational alert rate for absorp-
tion or permeability problems and a low measured incidence of poor turbidimetric solubility of about
10%. The calculated profiles and alert rates of compounds made in medicinal chemistry laboratories
can be compared to those of drugs and the profiles
can be compared on a project by project basis. Within the physical property manifold of ‘mar-
keted drugs’ we would expect a poor correlation of
our turbidimetric solubility data with literature
thermodynamic solubility data since the properties of ‘drugs’ occupy only a small region of property space
relative to what is possible in synthetic compounds and HTS ‘hits’. Our turbidimetric solubilities for
drugs are almost entirely at the top end of a relatively narrow solubility range, whereas from a thermodynamic viewpoint the drugs in our list cover a wide spectrum of solubility. We caution that
turbidimetric solubility measurements are most defi- nitely not a substitute for careful thermodynamic solubility measurements on well characterized crys- talline drugs and should not be used for decision making in a development setting.
2. IS. High throughput .rcrrening hits, ca1culatiotz.t
and soluhilil~ tnea.sc~retnet2t.s
Calculated properties and measured turbidimetric solubilities for the best compounds identified as
‘hits’ in our HTS screens are in accord with the
hypothesis that the physico-chemical profiles of leads
have changes from those in the pre-1989 time period. Nearly 100 of the most potent ‘hits’ from OUT high throughput screens were examined computationally and their turbidimetric solubilities were measured.
The profiles are strikingly different from those of the
353 drugs we studied. The HTS hits are on average more lipophilic and less soluble than the drugs. The 96 compounds we measured were the end product of
detection in HTS screens and secondary in vitro evaluation. These were the compounds highlighted in
summaries and which captured the chemist’s interest
with many ICSOs clustered in the 1 FM range. As such, they are the product of a biological testing process and a chemistry evaluation as to interesting
subject matter. Average MLogP for the HTS hits was a full log unit higher than for the drugs and the
average MWT was nearly 50 Da higher. By contrast, there was little difference in the number of hydrogen
bond donors and acceptors. The distribution curves for MLogP and MWT are roughly the same shape for the HTS hits and drugs but the means are shifted upwards in the HTS hits with a higher distribution of
compounds towards the unfavorable range of physico-chemical properties. The actual averages,
HTS vs. Drug are: MLogP, 2.81 vs. 1.79; MWT, 366 vs. 295; sum of OH NH, I.80 vs. 2.01; sum of N and
0, 5.4 vs. 4.69.
2.16. The triud of potency, solubility and
permeubilit?;
Acceptable drug absorption depends on the triad of dose, solubility and permeability. Our computa- tional alert does not factor in dose, i.e. drug potency.
It only addresses properties that are related to
potential solubility and permeation problems and it does not allow for a very favorable value of one parameter to compensate for a less favorable value of another parameter. In a successful marketed drug, one parameter can compensate for another. For example. a computational alert is calculated for azithromycin, a successful marketed antibiotic. In azithromycin, which has excellent oral activity, a very high aqueous solubility of 50 mg/ml more than
counterbalances a very low absorption rate in the rat intestinal loop of 0.001 min-‘. Poorer permeability in orally active peptidic-like drugs is usually com-
pensated by very high solubility. Our solubility
guidelines to our chemists suggest a minimum
thermodynamic solubility of 50 kg/ml for a com- pound that has a mid-range permeability and an average potency of 1.0 mg/kg. These solubility
guidelines would be markedly higher if the average
compound had low permeability.
2.17. Protocols for measuring drug .solubility in a
discovery setting
The method and timing of introduction of the drug
into the aqueous media are key elements in our
discovery solubility protocol. Drug is dissolved in
DMSO at a concentration of 10 kg/p.1 of DMSO which is close to the 30 mM DMSO stock con-
centration used in our own biology laboratories. This is added a microlitre at a time to a non-chloride
containing pH 7 phosphate buffer at room tempera-
ture. The decision to avoid the presence of chloride was a tradeoff between two opposing considerations. Biology laboratories with requirements for iso-os-
motic media use vehicles containing physiological levels of saline (e.g. Dulbecco’s phosphate buffered saline) with the indirect result that the solubility of
HCl salts (by far the most frequent amine salt from
our chemistry laboratories) can be depressed by the common ion effect. Counter to this consideration, is the near 100% success rate of our pharmaceutical groups in replacing problematical HCI salts with
other salts not subject to a chloride common ion effect. We chose the non-chloride containing medium to avoid pessimistic solubility values resulting from a historically very solvable problem.
The appearance of precipitate is kinetically driven and so we avoid a short time course experiment where we might miss precipitation that occurs on the
type of time scale that would affect a biological
experiment. The additions of DMSO are spaced a minute apart. A total of 14 additions are made. These correspond to solubility increments of < 5 Kg/ml to a top value of > 65 p,g/ml if the buffer volume is 2.5 ml (as in a UV cuvette). If it is clear that precipitation is occurring early in the addition se- quence, we stop the addition so that we have two consecutive readings after the precipitate is first detected. Precipitation can be quantified by an ab- sorbance increase due to light scattering by precipi- tated particulate material in a dedicated diode array
UV machine. The sensitivity to light scattering is a function of the placement of the diode array detector
relative to the cuvette and differs among instruments.
We found that the array placement in a Hewlett Packard HP8452A diode array gives high sensitivity to light scattering. Increased UV absorbance from
light scattering is measured in the 600-820 nm range
because most drugs have UV absorbance well below this range.
In its simplest implementation, the precipitation
point is calculated from a bilinear curve fit to the
Absorbance (I’ axis) vs. ~1 of DMSO (x axis) plot. The coordinates of the intersect point of the two line
segments are termed X crit and Y crit. X crit is the microlitres of DMSO added when precipitation
occurs and Y crit is the UV Absorbance at the
precipitation point. The concentration of drug in DMSO (10 pg/ml) is known. The volume of
aqueous buffer (typically 2.5 ml in a cuvette) is known so the drug concentration expressed as pg of drug per ml buffer at the precipitation point is readily
calculated. The volume percent aqueous DMSO at
the precipitation point is also reported. Under our assay conditions it does not exceed 0.67% for a turbidimetric solubility of > 65 pg/ml. The uppet
solubility limit is based on the premise that for most projects permeability is not a major problem and that solubility assays will most often be requested for
poorly soluble compounds. In the absence of poor permeability, solubilities above 65 kg/ml suggest that if bio-availability is poor, solubility is not the
problem.
2.18. Technical considerations und signal
processing
In our experience, most UV active compounds made in our Medicinal Chemistry labs have UV peak maxima below 400 nm. Approximation to a Gaus-
sian form for absorbance peaks allows an estimate for the UV absorbance at long wavelength from the peak maximum and peak width at half height. A soluble compound with maximum absorbance at 400 nm and extinction coefficient of 10 000 and peak width at half height of 100 nm at a concentration of 400 kg/ml (well above the maximum for our assay) has calculated absorbance of 0.000151 at 600 nm.
The sensitivity of UV absorbance measurements to
light scattering is largely a function of how closely the diode array is positioned to the UV cuvette and varies among manufacturers. The HP89532 DOS
software detects a curve due to light scattering by
fitting the absorbance over a wavelength range to a power curve of the form. Abs = k X nm-“, where k
is a constant, nm = wavelength Values for ‘n’ were examined in a total of 45
solubility experiments. The last scan in each solu-
bility series was examined since precipitation is most
likely at the highest drug concentration. In this 45 assay series precipitation was not observed in 10
assays (as assessed by values of n > 0). Positive values of n ranged as high as 5.054 in the 35 assays
in which precipitation occurred. Once precipitation occurred, all scans in an assay sequence could be tit
with a power curve. The overall absorbance increase due to light scattering can be quite low. In most of
the 45 assays, the total absorbance increase at 690 nm (due to precipitate formation) was in the OD range O-0.01. Half the absorbance increases were in
the range O-0.001. Measurements within these very
small ranges quantitate the precipitation point. Problems in determining the precipitation point
occur when a compound is intensely colored since colored compounds may be miscalled as insoluble. In collaboration with Professor Chris Brown at the
University of Rhode Island, we implemented a fast fourier transform (FFT) signal processing procedure to enhance assay sensitivity and to avoid false
positive solubility vaiues due to colored compounds 1201. The absorbance curve due to light scattering
has an apparent peak width at half height which is much wider than the apparent peak width at half
height for a typical UV absorption curve. An analysis procedure that is sensitive to the degree of curvature can be used to differentiate color from light scatter- ing. The even wavelength spacing in our diode array UV means that the absorbance vs. wavelength matrix in each scan can be treated as if it were a time series
(which it really is not). In a time series, the early terms in an FFT describe components of low curva- ture (low frequency). An FFT over a 256 nm range (566-820 nm) generates 128 absorbance values which in turn generates 128 FFT terms. FFT term 1 describes the baseline shift. By plotting the real
component of FFI term I or term 2 vs. DMSO addition, the false positive rate from color is much reduced and we detect the onset of precipitation as if
C.A. Lipinski et al. I Advanced Drug Delivery Reviews 23 (1997) 3-2.5 17
we were plotting absorbance at a single wavelength
vs. absorbance. An alternative to the use of a dedicated diode
array UV is to use one of a number of relatively inexpensive commercially available nephelometers.
The solubility protocol using a nephelometer as the signal detector is identical to that using a UV
machine. We have experience using a HACH AN2100 as a turbidity detector. A nephelometer has
the advantage that colored impurities do not cause a
false positive precipitation signal and so signal processing is avoided. The disadvantage is the larger
volume requirement relative to a UV cuvette. The
HACH unit uses inexpensive disposable glass test tubes that can be as small as 100 mm X 12 mm. The
use of even smaller tubes and the resultant advantage
of reduced volume is precluded by light scattering from the more sharply curved surface of a smaller
diameter tube.
Using nephelometric turbidity unit (NTU) stan- dards, the threshold for detection using a UV de-
tector-based assay is 0.2 NTUs and a 0.4 NTU
standard can be reliably detected vs. a water blank. Turbidity standards in the range 0.2-2 NTU units
suffice to cover the scattering range likely to be
detected in a solubility assay. Some type of signal detector is necessary if light scattering is the ana- lytical signal used to detect precipitation. For exam- ple, a 1.0 NTU standard was our lower visual
detection limit using a fiber optic illuminator to visualize Tyndall light scattering. The European
Pharmacopoeia defines the lowest category of tur- bidity - ‘slight opalescence’ on the basis of mea- sured optical density changes in the range 0.0005-
0.0156 at 340-360 nm. These optical density read-
ings correspond to NTU standards well below 1 .O (in the 0.2-0.4 range) in our equipment.
3. Calculation of absorption parameters
3. I. Overcall approach
The four parameters used for the prediction of potential absorption problems can be easily calcu- lated with any computer and a programming lan-
guage that supports or facilitates the analysis of molecular topology. At Pfizer, we began our pro- gramming efforts using MDL’s sequence and
MEDIT languages for MACCS and have since
successfully ported the algorithms to Tripos’ SPL
and MDL’s ISIS PL languages without difficulty. The parameters of molecular weight and sum of
nitrogen and oxygen atoms are very simple to calculate and require no further discussion. Likewise,
the calculation of the number of hydrogen-bond
acceptors is simply the number of nitrogen and
oxygen atoms attached to at least one hydrogen atom in their neutral state.
3.2. MLogP. Log P by the method of Moriguchi
The calculation of log P via the method of
Moriguchi et al. [l l] required us to make some assumptions that were not clear from the rules and
examples in the two papers describing the method [ 11,121. Therefore, more detailed discussion on how
we implemented this method is necessary.
The method begins with a straightforward count- ing of lipophilic atoms (all carbons and halogens with a multiplier rule for normalizing their contribu-
tions) and hydrophilic atoms (all nitrogen and oxy- gen atoms). Using a collection of 1230 compounds,
Moriguchi et al. found that these two parameters
alone account for 73% of the variance in the experimental log Ps. When a ‘saturation correction’
is applied by raising the lipophilic parameter value to the 0.6 power and the hydrophilic parameter to the 0.9 power, the regression model accounted for 75% of the variance.
The Moriguchi method then applies 11 correction factors, four that increase the hydrophobicity and seven that increase the lipophilicity, and the final
equation accounts for 91% of the variance in the
experimental log Ps of the 1230 compounds. The correction factors that increase hydrophobicity are:
1. UB, the number of unsaturated bonds except for those in nitro groups. Aromatic compounds like benzene are analyzed as having alternating single and double bonds so a benzene ring has 3 double bonds for the UB correction factor, naphthalene has a value of 5;
2. AMP, the correction factor for amphoteric com- pounds where each occurrence of an alpha amino acid structure adds 1.0 to the AMP parameter, while each amino benzoic acid and each pyridine carboxylic acid occurrence adds 0.5;
3. RNG, a dummy variable which has the value of 1.0 if the compound has any rings other than
benzene or benzene condensed with other aro- matic, hetero-aromatic, or hydrocarbon rings;
4. QN, the number of quaternary nitrogen atoms (if the nitrogen is part of an N-oxide, only 0.5 is
added);
The seven correction factors that increase lipo-
philicity are:
I. PRX, a proximity correction factor for nitrogen and oxygen atoms that are close to one another
topologically. For each two atoms directly bonded to each other, add 2.0 and for each two atoms
connected via a carbon, sulfur, or phosphorus
atom, add I.0 unless one of the two bonds connecting the two atoms is a double bond, in
which case, according to some examples in the
papers, you must add 2.0. In addition, for each carboxamide group, we add an extra I.0 and fat
each sulfonamide group, we add 2.0;
2. HB, a dummy variable which is set to 1.0 if there are any structural features that will create an
internal hydrogen bond. We limited our programs to search for just the examples given in the Moriguchi paper [ I I ] as it is hard to determine
how strong a hydrogen bond has to be to affect
lipophilicity: 3. POL, the number of heteroatoms connected to an
aromatic ring by just one bond or the number of
carbon atoms attached to two or more
heteroatoms which are also attached to an aro-
matic ring by just one bond; 4. ALK, a dummy parameter that is set to I .O if the
molecule contains only carbon and hydrogen atoms and no more than one double bond;
5. N02, the number of nitro groups in the molecule: 6. NCS, a variable that adds 1.0 for each isothio-
cyanate group and 0.5 for each thiocyanate group;
7. BLM, a dummy parameter whose value is I.0 if there is a beta lactam ring in the molecule.
Log Ps, calculated by our Moriguchi-based com- puter program for a set of 235 compounds were less accurate than the calculated log Ps (CLogPs) from Hansch and Leo’s Pomona College Medicinal
Chemistry Project MedChem software distributed by Biobyte. The set of 235 was chosen so that the CLogP calculation would not fail because of missing
fragments. Our implementation of the Moriguchi
method accounts for 83% of the variance with a standard error of 0.6 whereas the Hansch values
account for 96% of the variance with a standard error of 0.3. The advantages of the Moriguchi method are
that it can be easily programmed in any language so
that it can be integrated with other systems and it
does not require a large database of parameter values.
4. The development setting: prediction of aqueous thermodynamic solubility
The prediction of the aqueous solubility of drug
candidates may not be a primary concern in early screening stages, but the knowledge of the thermo-
dynamic solubility of drug candidates is of paramount importance in assisting the discovery, as well as the development, of new drug entities at later stages. A poor aqueous solubility is likely to result in
absorption problems, since the flux of drug across the intestinal membrane is proportional to its con-
centration gradient between the intestinal lumen and the blood. Therefore even in the presence of a good permeation rate a low absorption is likely to be the
result. Conversely. a compound with high aqueous solubility might be well absorbed, even if it posses-
ses a moderate or low permeation rate.
Formulation efforts can help in addressing these problems, but there are severe limitations to the absorption enhancement that can be realistically
achieved. Stability and manufacturing problems also have to be taken into account since it is likely that an
insoluble drug candidate may not be formulated as a
conventional tablet or capsule, and will require a less conventional approach such as, for example, a soft gel capsule. Low solubility may have an even greater impact if an i.v. dosage form is desired. Obviously, a tnethod for predicting solubility of drug candidates at an early stage of discovery would have a great
impact on the overall discovery and development process.
Unfortunately the aqueous solubility of a given
C.A. Lipinski et cd. I Advanced Drug Drlivep Revirws 23 (1997) 3-2.5 19
molecule is the result of a complex interplay of
several factors ranging from the hydrogen-bond
donor and acceptor properties of the molecule and of water, to the energetic cost of disrupting the crystal lattice of the solid in order to bring it into solution
(‘fluidization’) (241. In any given situation, not all the factors may play
an important role and it is difficult to predict the
solubility of a complex drug candidate, on the basis of the presence or absence of certain functional groups. Conformational effects in solution may play
a major role in the outcome of the solubility and cannot be accounted for by a simple summation of
‘contributing’ groups. Thus, any method which would aim at predicting
the aqueous solubility of a given molecule would have to take into account a more comprehensive ‘description’ of the molecule as the outcome of the
complex interplay of factors. The brief discussion of the problem outlined above
can be summarized by considering the three basic quantities governing the solubility (S) of a given
solid solute:
S = f( Crystal Packing Energy + CavitationEnergy
+ Solvation Energy)
In this equation, the crystal packing energy is a (endoergic) term which accounts for energy neces- sary to disrupt the crystal packing and to bring
isolated molecules in gas phases, i.e. its enthalpy of sublimation. The cavitation energy is a (endoergic)
term which accounts for the energy necessary to
disrupt water (structured by its hydrogen bonds) and to create a cavity into which to host the solute
molecule. Finally, the solvation energy might be defined as the sum (exoergic term) of favorable
interactions between the solvent and the solute. In dealing with the prediction of the solubility of
crystalline solids’, a first major hurdle to overcome is the determination or estimation of their melting point or, better, of their enthalpy of sublimation. At present no accurate and efficient method is available to predict these two quantities for the relatively
complex molecules which are encountered in the
‘Since the vast majority of drug molecules and most substances of
pharmaceutical interest are crystalline solids, this discussion will
focus on the predxtion of the solubility of crystalline solids.
pharmaceutical research. Gavezzotti’ 1261 has dis-
cussed this point in a review article on the predic-
tability of crystal structures and he states that ‘...the melting point is one of the most difficult crystal properties to predict.’ This author has pioneered the use of computational methods to predict crystal
structures and polymorphs and, consequently, prop- erties such as melting point and enthalpy of sublima-
tion. A commercially available program has been
recently developed [27] but the use of these ap- proaches is still far from being routine and from
being useful in a screening stage for a relatively
large number of compounds, all of which possess a relatively high conformational flexibility.
Thus, although there are several approaches to estimating and predicting the solubility of organic compounds, the authors of this article are of the
opinion that none of the presently available methods can truly be exploited for a relatively accurate
prediction of solubility, if the target of the prediction
is the solubility of complex pharmaceutical drug candidates. Although the judicious application of
some these approaches might be useful for ‘rank- ordering’ of compounds and prioritization of their synthesis, we are not aware of any such systematic
use of estimation methods.
The sections that follow will discuss available methods, taking into account the second and third terms of the above relationship and the feasibility of their assessment a priori, and they will be treated as
one term since the available methods consider the interactions in solution as the (algebraic) sum of the two terms and their contributors. This discussion is
by no means exhaustive but it is rather intended as
an overview of the methods available as seen, in particular, from a pharmaceutical perspective.
4.2. LSERs and TLSER methods
Linear Solvation Energy Relationships (LSERs), based upon solvatochromic parameters, have the advantage of a good theoretical background and offer a correlation between several molecular properties, and a solute property, SP. Several LSERs have been developed over the past few years and they seem to work well for predicting a generalized SP for a series
‘The program PROMET is available from Professor Gavezzotti.
University of Milan. Italy.
of solutes in one or more (immiscible) phases. Most
notably, the work of Abraham et al. [281 has generated an equation of the general type:
LogSP = c + rR, + a_.%~~ + h@f + .STT~
+ nv,
where c is a constant, R, is an excess molar
refractivity, &y:l and _Y@y are the (summation or ‘effective’) solute hydrogen-bond acidity and basici-
ty, respectively, nTT:, ” is the solute dipolarity-polar-
izability and V, is McGowan’s characteristic volume [29]. The main problem encountered when using parameterized equations is that such quantities (pa- rameters or descriptors) cannot easily be estimated.
from structures only, for complex multi-functional
molecules such as drug candidates, especially if they
are capable of intra-molecular hydrogen bonding, as is often the case. Nevertheless. the method was
successfully applied to the correlation between the solvatochromic parameters described above and the
aqueous solubility of relatively simple organic non
electrolytes [30]. More recently, Kamlet [31] has published equa-
tions describing the solubility of aromatic solutes
including polycyclic and chlorinated aromatic hydro- carbons. In these equations a term accounting for the crystal packing energy was introduced, and the
equation has the general form:
log S,,.(aromatics) = 0.24 - 5.28V,
100 + 4.03&
+ 1.53a,,, - 0.0099(‘?2./7.
- 25)
where V, is the intrinsic (van der Waals) molar volume of the solute, the other parameters are defined as above and the subscript nz indicates a non self-associating solute monomer. It is interesting to
note that the term 0.0099(m.p.-25) is used, in the words of the author. ‘to account for the process of conversion of the solid solute to super-cooled liquid at 25°C.’ This term is therefore related to the crystal packing energy mentioned earlier, albeit representing the conversion from a solid to a ‘super-cooled’ liquid, not to isolated molecules in gas phase. The author finds the above term ‘robust’ in its statistical significance and it should be noted that coefficient of 0.0099 implies that a variation of less than one order
of magnitude will be observed for variations in
melting points of less than 100°C. This finding might be exploited in a series of close
structural analogs where a large variation in melting
points ( > 100°C) is not expected (as might often be the case) and the ‘solution behavior’ could be
estimated by solvatochromic parameters. Thus, with
some error, the prioritization of more soluble syn- thetic targets might be achieved, since the relative ( ‘rank-order’) solubility of structurally close analogs
may be all that it is sought at an early stage. However this prioritization would rely on the as- sumption that variations in structural properties which bring about a (desired) lowering of the crystal
packing energy, would not significantly and adverse- ly alter the properties of a molecule with respect to
ita solvation in water. If the lower crystal packing
energy is the result, for example, of a lower hydro- gen-bond capability, a diminished solvation in water
may offset the lowering of the crystal packing
energy. Even with the assumption described above, the
estimation of a relatively good rank-ordering of aqueous solubilities would still require the determi- nation of solvatochromic parameters which is gener-
ally achieved through the determination of several partition coefficients. On the other hand, descriptor
values for several fragments (functional groups) are available and they may be used to calculate the ‘summation’ parameters for the molecules of inter-
est. This process is not without caveats though, as a very judicious choice of the ‘disconnection pattern’
must be made to obtain reliable results. In a recent paper describing the partition of solutes across the
blood-brain barrier, Abraham et al. 1321 reported the calculation and use of these descriptors for com- pounds of pharmaceutical interest but he warned about the possibility of inter-molecular hydrogen bonding, which may be a source of error if not present in the ‘reference’ compounds, and pointed
out the fact that these correlations are best used within the descriptors range used to generate them.
Some authors have reported the calculation of quantities related to those descriptors, via ab initio [ 33-351 or semi-empirical methods [36,37]. The equations stemming from computed values have been termed TLSERs (Theoretical Linear Solvation Energy Relationships) 1361. However, we are not aware of any application of this approach to a series
C.A. Lipinski et 01. I Advanced Drug Delivery Reviws 23 (1997) .7-25 21
of complex multifunctional compounds, and these
types of correlations are likely to be difficult for
these compounds, due to the relatively high level of
computation involved. Ruelle and Kesselring and colleagues 138-401
reported a multi-parameter equation, qualitatively
similar to the LSERs described above. This equation
attempts to predict solubility by using terms which
account for the quantities that play a role in the process. It does contain a solute ‘fluidization’ term
(endoergic cost of destroying the crystal lattice of a
solid) and other terms describing the hydrophobic effect, hydrogen bond formation between proton-
acceptor solutes and proton-donor solvents, and the
H-bond formation between amphiphilic solutes and proton acceptor and/or proton-donor solvents as well as the auto-association of the solute in solution.
Although this equation takes into account the free
energy changes involved in the dissolution process,
in our opinion its complexity prevents its use for multifunctional molecules. The examples reported
address simple hydrocarbons or mono-functional molecules and much emphasis is placed on organic
(associated and non-associated) solvents. In many such cases. approximations leading to the cancella-
tion of some term, can be made but, if an attempt to predict the solubility of complex drug candidates in
water is made, all those terms might be present at the same time and thus it would be very difficult to treat solubility within the framework of this equation.
4.3. LogP and AQUAFAC methods
Prominent in this area is the work of Yalkowski
[41] who has published a series of papers describing the prediction of solubility using LogP (the logarithm
of the octanol/water partition coefficient) and a term describing the energetic cost of the crystal lattice disruption. However Yalkowski’s work is largely based on the prediction or estimation of the solubility of halogenated aromatic and polycyclic halogenated aromatic hydrocarbons [42], due to their great en-
vironmental importance. The general solubility equa- tion, for organic non-electrolytes is reported below.
log S,“.“, = - dS,,,(m.p. - 25)
1364 - log P + 0.80
In this equation, AS,,, is the entropy of melting and
m.p. is the melting point in “C. The signs of the two
terms considered are physically reasonable, since an
increase in either the first term (higher crystal packing energy) or in LogP (more lipophilic com- pound), would cause a decrease in the observed
(molar) solubility S,. In a recent paper [43], this
author discusses the predictive use of the above
equation and, in particular, the prediction of activity
coefficients. The latter is a term which accounts for
deviations from ideal solubility behavior due to differences in size and shape, but also in hydrogen
bonding ability, between the solute and the solvent.
The conclusion is that, among methods based upon solvatochromic parameters, or simply based on mo-
lecular volume, molecular weight or regular solution
theory, the estimation of the activity coefficient is
best achieved by using the LogP method. Many computational methods are indeed available
to address the prediction of LogP and the aqueous
solubility of complex molecules. A well known and
widely used program to predict LogP values is
CLogP 1441 which uses a group-contribution ap-
proach to yield a LogP value. Another method, developed by Moriguchi et al. [ 111, which uses atomic constants and correction factors to account
for different atom types is discussed in detail in Section 3.2. We have observed that, in the daily
practice of pharmaceutical sciences, both methods have their ‘outliers’ but methods based on fragmental constants tend to fail, in the not infrequent instances
where appropriate constants are not available.
However, LogP prediction aside. the method reported by Yalkowski was developed on a data set
largely based upon rigid, polycyclic and halogenated
aromatic compounds and does not seem to easily yield itself to the prediction of complex pharma- ceutical compounds. The basic difficulty is that while
LogP could be estimated albeit with some error by computational approaches, the melting point and entropy of melting are still difficult to calculate or even simply to estimate. Yalkowski discusses this point in several papers [42,45,46] and shows the
relationship between the entropy of fusion and the molecular rotational and translational entropies. Some rules are offered for the estimation of entropy, but the work is limited to relatively simple mole- cules. The melting point prediction is also discussed and a computational approach, based on molecular properties such as eccentricity (the ratio between the
maximum molecular length and the mean molecular diameter) is proposed. However, the calculation of
such properties may be easy to perform on simple
polychlorinated biphenyls, but would not easily be
applicable for complex drug candidates. A similar approach to solubility predictions using
a group-contribution method has been implemented in the CHEMICALC-2 program [47], which calcu-
lates LogP and log 1 /S where S is the molar aqueous
solubility. This program uses several different algo-
rithms to calculate log 1 /S depending on the com- plexity and nature of the molecule, and requires
knowledge of the melting point, T,,,. If T,,, is not available, the program calculates the solubility of the
super-cooled liquid at 25°C. In the case of complex
molecules, fragmental constants may be missing
from its database and poor results are obtained. We
have used this program to some extent and we are
not encouraged by the correlation between ‘pre- dicted’ and experimental solubility.
Yalkowski and colleagues (481 have more recently discussed an improvement of the AQUAFAC (AQUeous Functional group Activity Coefficients)
fragmental constant method. In this work, the authors describe a correlation between the sum of fragmental
constants of a given molecule and the activity coefficient, defined as a measure of the non-ideality
of the solution. The knowledge or estimation of AS,,, and m.p. is necessary, but the method seems to be
somewhat better than the general solubility equation based on LogP values. Yalkowski explains this by
pointing out that these group contribution constants were derived entirely from aqueous phase data and they should perform better than octanol-water parti-
tion coefficients. We concur with this explanation since it is known that the octanol-water partition
coefficients are rather insensitive to the hydrogen- bond donor capability of the solute. Furthermore the authors point out the fact that molecules like small carboxylic acids are likely to dimerize in octanol,
while in water they would not. The solubility equation derived using the
AQUAFAC coefficients is reported below.
log qm, = - &,&1./I. - 25)
_ I.364 %4,
where q, is the group contribution of the ith group and n, is the number of times the ith group appears in the molecule. The negative sign of the second
term stems from the fact that the constant of polar groups (e.g. OH= - 1.81) has a negative sign and a
net negative sign of the summation of contributors
would yield an overall positive contribution to
solubility. However while this method might be of
simple application, its scope seems limited to mole- cule containing relatively simple functional groups,
and the objections to the use of group contribution methods, which do not consider conformational
effects, remain.
4.4. Other ccdxlation methods
Bodor and Huang 1491 and Nelson and Juts ]50]
have reported methods based entirely on calculated geometric, electronic and topological descriptors, for
a series of relatively simple liquid and solid solutes.
We favor these methods as truly a priori predic-
tions based on molecular structures only, but some questions arise when the compounds have conforma-
tional flexibility and multiple functional groups, and some of the descriptors will depend upon the par- ticular conformation chosen. As it is generally true for many QSAR approaches, there is uncertainty
about the actual predictive value of a test set which does not include a wide variety of compounds and, in Bodor’s training set of 331 compounds we fail to
recognize with few exceptions represented by rigid steroids, complex multifunctional molecules. Fur-
thermore a large number of the compounds used are liquids or gases at ambient temperature.
Bodor’s method involves the calculation of 18
descriptors, among which are the ovality of the
molecule, the calculated dipole moment, and the square root of the sum of squared charges on oxygen atoms, but it does yield a good correlation for the 331-compound set. The predictive power of the model is illustrated by a table of 17 compounds, but most of them are rigid aromatics, although a reason- ably good prediction is offered for dexamethasone.
The latter however is an epimer of betamethasone which is present in the training set, and it is difficult to predict the robustness of the correlation with regard to its application to a truly diverse set of molecules. Similar considerations could be extended to the work by Nelson and Jurs, which is also based on calculated descriptors and it does not seem to involve any polyfunctional molecule or any solid compound at 25°C. Overall the correlation is good
C.A. Lipinski et ul. I Advmced Drug Drlivep Revirws 2.3 (1997) 3-25 23
but the caveats on its application to drug-like com-
pounds remain, as well as our objections on the ease
of calculation of the parameters for compounds of
pharmaceutical interest. Finally, Bodor et al. [25] and Yalkowski and
colleagues [5] have reported the use of neural
networks to develop correlations using the calculated
parameters discussed above or the AQUAFAC co- efficients, respectively. While we have no direct
experience with the use of neural networks, we are
of the opinion that it may not be a trivial task to set up and ‘train’ a neural network and the superiority of this approach in comparison to ‘conventional’ regres- sion techniques may be more apparent than real. Indeed Bodor reports a similar standard deviation for
the prediction using the neural network or regression analysis [49] on the same data set, and the use of a neural network does not appear to offer any advan- tage over the regression analysis.
5. Conclusion
Combinatorial chemistry and high throughput screening (HTS) techniques are used in drug re-
search because they produce leads with an efficiency that compares favorably with ‘rational’ drug design
and, perhaps more importantly, because these tech- niques expand the breadth of therapeutic oppor- tunities and hence the leads for drug discovery. Established methodology allows the medicinal chem-
ist, often in a relatively short time, to convert these novel leads to compounds with in vitro potency suitable to a potential drug candidate. This stage of
the discovery process is highly predictable. However, the majority of drugs are intended for oral therapy and introducing oral activity is not predictable, is time and manning expensive and can easily consume more resources than the optimization of in vitro activity. The in vitro nature of HTS screening
techniques on compound sets with no bias towards properties favorable for oral activity coupled with
known medicinal chemistry principles tends to shift HTS leads towards more lipophilic and therefore generally less soluble profiles. This is the tradeoff in HTS screening. Efficiency of lead generation is high, and therapeutic opportunities are much expanded, but the physical profiles of the leads are worse and oral activity is more difficult. Obtaining oral activity
can easily become a rate-limiting step and hence methods which allow physico-chemical predictions
from molecular structure are badly needed in both early discovery and pharmaceutical development
settings. Computational methods in the early discovery
setting need to deal with large numbers of com- pounds and serve as filters which direct chemistry
SAR towards compounds with greater probability of
oral activity. These computational methods become particularly important as experimental studies be- come more difficult because compounds are avail-
able for physico-chemical screening in only very small quantities and in non-traditional formats. Early discovery methods deal with probabilities and not
exact value predictions. They enhance productivity by indicating which types of compounds are less likely to be absorbed and which are more likely to
require above average manning expenditures to
become orally active. Calculations, however impre- cise, are better than none when choices must be
made in the design or purchase of combinatorial
libraries. Drug discovery requires a starting point - a lead. Hence the current literature correctly focuses on improving in vitro activity detection by optimiz- ing chemical diversity so as to maximize coverage of three-dimensional receptor space. Assuming this goal
is not compromised by physico-chemical calcula- tions, we believe a competitive advantage accrues to
the organization that can identify compound sets likely to give leads more easily converted to orally active drugs.
Methods in the pharmaceutical developmental setting deal with much smaller numbers of com-
pounds. Here, a more accurate prediction is computa- tionally complex because exact values rather than probabilities are important, and because the predic- tion of crystal packing energies is at present extreme- ly difficult. The problem of polymorphism, common in pharmaceutical research, which may have been
deferred in the discovery setting has to be addressed in the development setting. Currently, only approxi- mate estimates of the solubility of multifunctional and conformationally flexible drug candidates are possible and these need to be supported by physical measurements which provide experimental ‘feed-
back’ on analogs in a particular class of compounds. In our view, a priori solubility estimation methods like Bodor’s multi-parameter equation (491 are the
current best choice, but some of the required prop- erties are not easily computed without a preliminary optimization of preferred conformations and good
initial estimates. The accurate prediction of the
solubility of complex multifunctional compounds at the moment still remains an elusive target. The
requirements for high accuracy and the complexity of possible studies in the drug developmental setting means that even small changes towards poorer, but
still acceptable, physico-chemical properties in com- pounds approaching candidacy can translate to high-
er developmental time and manning requirements. Moreover, there has not been the same level of
efficiency improvement in many developmental as- says as there has been in discovery screening. For
example, there is not the same level of efficiency
improvement in measuring accurate equilibrium solubility as there has been in the efficiency of
detecting leads. Medicinal chemists efficiently and predictably
optimize in vitro activity, especially when the lead has no key fragments missing. This ability will likely
be reinforced because the current focus on chemical diversity should produce fewer leads with missing
fragments. Oral activity prospects are improved
through increased potency, but improvements in solubility or permeability can also achieve the same
goal. Despite increasingly sophisticated formulation approaches, deficiencies in physico-chemical prop- erties may represent the difference between failure
and the development of a successful oral drug
produc __
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